Biology subjects

Shukla, K.

Publications and source records attributed to Shukla, K..

10 recordsLinked to original sources

bioRxiv2026

Immune-metabolic-redox ecosystems define spatially organized tumor states in head and neck squamous cell carcinoma.

Background: Spatial organization is increasingly recognized as a key determinant of tumor-immune interactions in head and neck squamous cell carcinoma (HNSCC). The GSE300147 Xenium spatial transcriptomic resource generated by McCord and colleagues established a framework for mapping spatially coordinated T-cell states in HNSCC. However, how tumor-enriched epithelial immune states relate to metabolic, redox, and stress-adaptive transcript programs remains incompletely defined. Methods: A secondary, data-driven reanalysis of GSE300147 was performed, focusing on 17 confirmed HNSCC Xenium sections after exclusion of a non-HNSCC ameloblastoma specimen. A total of 1,148,244 cells were analyzed, including 558,867 EpCAM+ tumor-enriched epithelial cells. Tumor-enriched epithelial cells were classified into Hot, Intermediate, and Cold states using a Composite Hotness framework integrating T-cell inflammatory signature score, checkpoint-associated signaling, CD274 expression, IFN/antigen-presentation signature score (IFN/AP), and tumor-immune proximity. Six metabolic ecosystem states, neighborhood profiling, spatial permutation testing, and an integrated Immune-Metabolic-Redox Ecosystem Score (IMRES) were then applied. Results: Immune activation was spatially heterogeneous across HNSCC sections. Immune-hot tumor-enriched epithelial regions showed not only inflammatory, checkpoint-associated, and antigen-presentation signature scores, but also coordinated metabolic, oxidative-redox, and stress-response transcript programs. IMRES, derived from available immune, metabolic, redox, and stress-response transcript components represented in the Xenium panel, increased progressively from Cold to Intermediate to Hot tumor-enriched epithelial states and was associated with NFE2L2, GDF15, HLA-DRA, CD274, KEAP1, and MDM2. Integrating IMRES with Composite Hotness identified a distinct Hot+IMREShigh ecosystem comprising 106,874 tumor-enriched epithelial cells. This state showed the strongest immune-active and stress-adaptive features and was positioned closer to immune populations than expected by random assignment. An alternative rank-based robustness analysis reproduced the IMRES-associated ecosystem axis and correlated with the original module-based score (Spearman r = 0.597). Conclusions: This secondary reanalysis extends the original spatial T-cell framework by defining a complementary tumor-centered immune-metabolic-redox ecosystem in HNSCC. IMRES provides a transcript-derived framework for identifying Hot+IMREShigh neighborhoods where immune activation, checkpoint signaling, metabolic remodeling, and stress adaptation converge, providing a hypothesis-generating framework for studying immune resistance and therapeutic vulnerability.

cancer biology↗

bioRxiv2026

Adaptive Charge Modulation Enables Focal, Selective Spinal Cord Stimulation

Clinical neuromodulation primarily employs near-field low frequency electrical stimulation to activate neurons in the immediate vicinity of the electrode. We introduce Adaptive Charge Modulation (ACM), a spatiotemporal, charge-balanced stimulation strategy that focuses activation at deep tissue sites distant from the stimulating contacts. We apply ACM to stimulate deep regions of the spinal cord, distant from dorsal root entry zones which are preferentially activated during low frequency stimulation (LFS). ACM applies multipolar, biphasic rectangular pulses to exceed activation thresholds in deeper neuronal populations, with reduced surface activation, potentially due to high-frequency suppression of neural activity. In epidural spinal cord stimulation in rats, ACM achieved single-muscle selectivity among fourteen monitored muscles with minimal co-activation of other muscles. Using simultaneous, high spatiotemporal resolution, 2,112-channel brain-spine recordings, we characterized the response latencies and pathways consistent with focal recruitment at depth. We observed chronic stability of the electrode and ACM in freely behaving animals over 68 days post-implantation. By enabling focal activation with epidural surface electrodes, ACM may expand the reach and precision of neuromodulation and neural interfaces.

bioengineering↗

bioRxiv2026

STARMAP: A 3D-informed framework for mapping functional regions in proteins to regulatory and cellular phenotypes

Artificial Intelligence (AI) has transformed biology by revealing patterns in large-scale datasets and predicting regulatory relationships. Yet even the most advanced models often fail to identify biologically meaningful mechanisms from statistical associations. This limitation arises not from algorithmic capacity but from the lack of mechanistically grounded input features. Our structure-informed framework Structure-based Topological Analysis of Regulatory and Molecular Activity Patterns (STARMAP) embeds protein three-dimensional structure and population-scale functional genomics data into a unified representation for mechanistic inference. By mapping over 1.5 million naturally occurring variants across [~]1,700 cancer cell lines onto protein structures, STARMAP was able to identify spatial clusters of variation associated with shifts in transcriptional regulatory networks and drug response phenotypes. This approach transforms natural genetic variation into a large-scale, structure-informed screen, enabling systematic discovery of regulatory relationships across the proteome and providing interpretable and testable models of cellular regulation.

genomics↗

bioRxiv2026

Amyloid precursor protein interacts with the mitochondrial phosphatase PGAM5 and regulates mitochondrial respiration

Amyloid Precursor Protein (APP) has been reported to partially localize to mitochondria, and mitochondrial dysfunction is a key feature of Alzheimers disease; however, the mechanisms linking APP to mitochondrial functions remain incompletely defined. In this study, we identified an interaction between APP and phosphoglycerate mutase family member 5 (PGAM5), a mitochondrial protein phosphatase. We confirmed their endogenous interaction in mouse brain tissue and determined that APP and PGAM5 are both present at mitochondria-ER contact sites (MERCS) and. Using in vitro binding assays, we demonstrate a direct interaction between the linker region of APP and a region of PGAM5 that includes the Kelch-like ECH-associated protein 1 (Keap-1) binding domain. PGAM5 is known to anchor a portion of Nuclear factor erythroid 2 p45-related factor 2 (Nrf2) through Keap1 at the outer mitochondrial membrane and regulates mitochondrial respiration and stress responses. We found that the Nrf2-regulated genes Hmox1 (Heme oxygenase-1) and Nqo1 (NADH:quinone oxidoreductase 1), which are involved in mitochondrial respiration, are downregulated in APP KO astrocytes. Accordingly, mitochondria isolated from the brains of APP knockout (KO) mice have impaired substrate-specific respiration and electron transport chain (ETC) function. Together, these findings suggest that APP supports mitochondrial respiration by binding to PGAM5 and modulating Keap1-Nrf2 signaling.

neuroscience↗

bioRxiv2025

sAPPalpha inhibits neurite outgrowth in primary mouse neurons via GABA B Receptor subunit 1a

Neurite outgrowth is essential for neural circuit formation and is tightly regulated by secreted factors and their receptors. The secreted extracellular domain of the amyloid precursor protein (sAPP) has been shown to modulate neurite outgrowth. Recently, the gamma amino butyric acid receptor type-B subunit 1a (GABABR1a) was identified as an sAPP binding partner that mediates its effects on synaptic transmission. Here, we investigated whether this interaction also regulates neurite outgrowth. In primary hippocampal neurons, the GABABR agonist baclofen reduced axon length; whereas, its antagonist CGP54626 increased axon length in primary hippocampal neurons. Moreover, GABABR1a knockout increased axon length and abolished the effect of baclofen. Application of sAPP reduced axon length, an effect that required the presence of both GABABR1a and the extension domain of sAPP, which mediates its binding to GABABR1a. Similarly, the APP 17mer peptide, which is sufficient to bind GABABR1a and mimic the effects of sAPP on synaptic transmission, reduced axon outgrowth in wildtype but not in GABABR1a-deficient neurons. Together, these findings indicate that the 1a isoform contributes to GABABR-dependent suppression of neurite outgrowth and mediates the inhibitory effect of sAPP on neurite outgrowth. Statement of SignificanceAmyloid precursor protein (APP) plays a central role in Alzheimers disease, yet its normal functions are not fully understood. In this study, we uncover a previously unrecognized role of the GABA B Receptor in mediating the inhibitory effects of sAPP on neurite outgrowth. These findings provide mechanistic insight into how disruptions in APP signaling could influence both normal brain development and pathological processes in neurodevelopmental disorders and Alzheimers disease.

neuroscience↗

bioRxiv2025

AI-Driven Variant Annotation for Precision Oncology in Breast Cancer

Interpreting the functional impact of genomic variants remains a major challenge in precision oncology, particularly in breast cancer, where many variants of unknown significance (VUS) lack clear therapeutic guidance. Current annotation strategies focus on frequent driver mutations, leaving rare or understudied variants unclassified and clinically uninformative. Here, we present an AI/ML-driven framework that systematically identifies variants associated with key breast cancer phenotypes, including ESR1 and EZH2 activity, by integrating genomic, transcriptomic, structural, and drug response data. Using DepMap and TCGA datasets, we analyzed >12,000 variants across breast cancer genomes, identifying structurally clustered mutations that share functional consequences with well-characterized oncogenic drivers. This approach reveals that mutations in PIK3CA, TP53, and other genes strongly associate with ESR1 signaling, challenging conventional assumptions about endocrine therapy response. Additionally, EZH2-associated variants emerge in unexpected genomic contexts, suggesting new targets for epigenetic therapies. By shifting from frequency-based to structure-informed classification, we expand the set of potentially actionable mutations, enabling improved patient stratification and drug repurposing strategies. This work provides a scalable, clinically relevant method to accelerate variant annotation, offering new insights into drug sensitivity and resistance mechanisms. Future validation efforts will refine these predictions and integrate clinical outcomes to guide personalized treatment strategies. Our findings highlight the transformative potential of AI/ML in redefining cancer variant interpretation, bridging the gap between genomics, functional biology, and precision medicine. Study HighlightsO_ST_ABSCurrent KnowledgeC_ST_ABSBreast cancer treatment decisions are increasingly guided by genomic profiling, yet most clinical actionability is based on frequent driver mutations (e.g., PIK3CA, TP53, ESR1). Many variants of unknown significance (VUS) remain unclassified, and current annotation methods are slow, relying on manual curation or low-throughput assays, leaving rare mutations uncharacterized. Study FocusThis study applies AI/ML-driven variant annotation to systematically identify mutations that drive key breast cancer phenotypes, such as ESR1 and EZH2 activity, beyond currently known mutations. By using structural and functional clustering, we assess whether rare and understudied mutations can be prioritized for clinical relevance. Key Findings[bullet] Analyzed >12,000 variants across breast cancer genomes, integrating multi-omic and structural data. [bullet]Identified strong ESR1-associated mutations in PIK3CA, TP53, and other genes, expanding the landscape of actionable mutations. [bullet]Discovered EZH2-associated variants in unexpected contexts, revealing potential epigenetic therapy targets. [bullet]Demonstrated that spatial clustering of mutations within proteins predicts functional consequences, even for rare mutations. Clinical and Translational Impact[bullet] Scalable AI-powered framework accelerates variant annotation and functional classification. [bullet]Enables faster identification of actionable mutations and improves patient stratification for targeted therapies. [bullet]Provides a data-driven approach to refine clinical trial design, expanding therapy options for patients lacking clear genomic-based treatment guidance.

bioinformatics↗

bioRxiv2024

Leveraging AI to Automate Detection and Quantification of Extrachromosomal DNA (ecDNA) to Decode Drug Responses

Traditional drug discovery efforts have largely focused on targeting rapid, reversible protein-mediated adaptations to undermine cancer cells resistance to therapy. However, cancer cells also exploit DNA-based strategies, typically viewed as slow, irreversible, and unpredictable changes like point mutations or the selection of drug-resistant clones. Contrary to this perception, extrachromosomal DNA (ecDNA) represents a form of DNA alteration that is rapid, reversible, and predictable, playing a crucial role in cancers adaptive response. In this study, we present a novel post-processing pipeline for the automated detection and quantification of ecDNA in Fluorescence in situ Hybridization (FISH) images using the Microscopy Image Analyzer (MIA) tool. Our approach is particularly designed to monitor ecDNA dynamics during drug treatment, providing a quantitative framework to understand how ecDNA enables cancer cells to swiftly and reversibly adapt to therapeutic pressure. This pipeline not only offers a valuable resource for researchers aiming to automate ecDNA detection in FISH images but also sheds light on the adaptive mechanisms of ecDNA in response to epigenetic remodeling agents like JQ1.

cancer biology↗

bioRxiv2023

Machine learning of three-dimensional protein structures to predict the functional impacts of genome variation

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants with unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations to disease-related phenotypes. These studies have made a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, comparable computational methods for variants to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a genes downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2)-related factor 2 (Nrf2) and c-MYC. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-MYC or Nrf2 transcriptional pathway activities. SignificanceThe vast majority of mutations are either unspecified and/or their downstream biological implications are poorly understood. We have created a method that utilizes protein structure to cluster mutations from population-scale repositories to predict downstream functional impacts. The broader impacts of this approach include advanced filtering of mutations that are likely to impact genome function.

bioinformatics↗

bioRxiv2020

Nitric oxide-inducible Fe-S cluster biogenesis enables Mycobacterium tuberculosis to persist in mice

The persistence of Mycobacterium tuberculosis (Mtb) is a major problem in managing tuberculosis (TB). Host-generated nitric oxide (NO) is perceived as one of the signals by Mtb to reprogram metabolism and respiration for persistence. However, the mechanisms involved in NO sensing and reorganizing Mtbs physiology are not fully understood. Since NO damages Fe-S clusters of essential enzymes, the mechanism(s) involved in regulating iron-sulfur (Fe-S) cluster biogenesis could help Mtb persist in host tissues. Here, we show that a transcription factor SufR (Rv1460) senses NO via its 4Fe-4S cluster and promotes persistence of Mtb by mobilizing the Fe-S cluster biogenesis system; suf operon (Rv1460-Rv1466). Analysis of anaerobically purified SufR by UV-visible spectroscopy, circular dichroism, and iron-sulfide estimation confirms the presence of a 4Fe-4S cluster. Atmospheric O2 and H2O2 gradually degrade the 4Fe-4S cluster of SufR. Furthermore, electron paramagnetic resonance (EPR) analysis demonstrates that NO directly targets SufR 4Fe-4S cluster by forming a protein-bound dinitrosyl-iron-dithiol complex. DNase I footprinting, gel-shift, and in vitro transcription assays confirm that SufR directly regulates the expression of the suf operon in response to NO. Consistent with this, RNA- sequencing of Mtb{Delta}sufR demonstrates deregulation of the suf operon under NO stress. Strikingly, NO inflicted irreversible damage upon Fe-S clusters to exhaust respiratory and redox buffering capacity of Mtb {Delta}sufR. Lastly, Mtb{Delta} sufR failed to recover from a NO-induced non-growing state and displayed persistence defect inside immune-activated macrophages and murine lungs in a NO-dependent manner. Data suggest that SufR is a sensor of NO that supports persistence by reprogramming Fe-S cluster metabolism and bioenergetics. HighlightsO_LIMycobacterium tuberculosis (Mtb) induces the expression of suf operon for Fe-S cluster biogenesis in response to nitric oxide (NO). C_LIO_LIWe found that a transcription factor SufR senses NO via its 4Fe-4S cluster and regulates the expression of the suf operon for Fe-S cluster biogenesis. C_LIO_LISufR-regulated Fe-S cluster biogenesis confers respiratory and redox features that promote recovery of Mtb from NO stress. C_LIO_LISufR activity is required to support the NO-dependent persistence of Mtb in macrophages and mice. C_LI

microbiology↗

bioRxiv2020

DCP, First Generation PET Radiotracer for Diagnosis of Radiation Resistant Head and Neck Cancer

Redox metabolism plays essential functions in the pathology of cancer. As tumor redox profiles uniquely reflect cancer stage and in select cases, therapeutic sensitivity, the capability to image redox molecular features is essential to improve diagnosis, treatment, and overall quality-of-life (QOL) of cancer patients. While a number of radiotracers for imaging redox metabolism have been developed, there are no reports of radiotracers for in vivo imaging of protein oxidation. Here we take the first step towards this goal and describe the synthesis and kinetic properties of a new positron emission tomography (PET) [18F]DCP radiotracer for in vivo imaging of protein sulfenylation. Time course biodistribution and PET/CT studies using xenograft animal models of Head and Neck Squamous Cell Cancer (HNSCC) demonstrate feasibility of diagnosing radiation resistant tumors, which display lower [18F]DCP signal. These findings are consistent with our previous reports of decreased protein sulfenylation in clinical specimens of radiation resistant HNSCC. We anticipate further development and implementation of this concept in clinical practice to improve the diagnosis of patients with radiation resistant tumors and the accuracy of prognosis for patients undergoing radiation treatment. Single Sentence SummaryThe study introduces a new PET radiotracer for profiling tumor protein oxidation as a prognostic indicator of resistance to radiation therapy.

cancer biology↗